The central idea of Attentive CutMix is to create a new training sample \((\tilde{x},\tilde{y})\)  given two distinct training samples \((x_1,y_1)\)  and \((x_2,y_2)\) . Here, \(x \in R^{W \times H \times C}\)  is the training image and \(y\)  is the training label. Similar to CutMix [1]}, we define this combining operation as,
\(\tilde{x} = \textbf {B} \odot x_{1} + (\textbf {1} - \textbf {B}) \odot x_{2}\) 
\(\tilde{y} = \lambda y_{1} + (1 - \lambda ) y_{2}\) 
